Metadata management is one of the foundational components of an enterprise data management initiative. Many organizations struggle with its incorporation into their data management processes. Metadata Management’s Relationship to Data Management Data Management is one of the hottest topics in our industry as Global 2000 companies and large government agencies are beginning to understand that without accurate, timely, and well-understood
The business value of text is immense, but it is not well understood by most organizations. Analysis of textual data can provide a wide variety of benefits. It is intuitively obvious to many people that there is great business value that can be derived from the analysis of text. But the relationship between business value and the analysis of text
Textual data is a valuable component for business analytics, but the responsibility for gathering and managing textual data is unclear. Consider textual processing for delivering business value. Where does the end user turn? The end user certainly recognizes the value of textual data. Intuitively the end user knows that there is a wealth of value to be found in text.
Text, the basis of language, can be considered to be unstructured by technicians. However, for others, text and language have a definite structure. It is the simplest of questions. Ask any technician if text – language – is structured or unstructured. If your technician is knowledgeable, the technician will assure you that text is unstructured. In fact the technician will
A managed metadata environment (MME) is the optimal approach to metadata architecture for meeting business and technical requirements Introduction Almost every corporation and government agency has already built, is in the process of building, or is looking to build a Managed Metadata Environment (MME), either as part of a metadata solution or within an enterprise data management initiative. Many organizations,
Project management requires the use of a solid framework and an experienced project manager to ensure the success of any project, any size, in any industry Introduction I was hooked on watching the Iron Chef – what got me interested in this show was the project manager in me. Watching these highly skilled chefs be provided with the exact same
Master data management continues to be plagued by poor data quality, costing many organizations time, money, lost customers, lost reputation and inefficiencies. Introduction In several surveys, taken by various research organizations about master data management and data quality, on average, 83% of respondents reported that their organizations have suffered problems due to poor master data. Business trends such as the ever-increasing
Metrics and the measurements they create are essential to the success of every data governance program and every data stewardship effort. Introduction Organizations that implement any new initiative must be able to measure its success so the program’s leadership can deliver progress reports to stakeholders and sponsors. Communicating success based on measured facts enables the program to demonstrate its effectiveness,
Abstraction is a powerful design tactic for creating flexible, robust and scalable data warehouse data models Introduction I remember meandering through the large galleries of a modern art museum, and stopping in my tracks in front of a very large canvas painted completely red with a single white dot in the center. The title was “City Skyline”, and as hard
Standards are different from guidelines. Standards are firm and must be followed. Successful data warehouses use standards Introduction Many dog owners give their dogs what they consider to be commands. They are really more like guidelines. (“Boscoe come!… pause, pause, pause… Well I guess Boscoe is busy with his chewy toy and doesn’t want to come just now.”) A number
